The strategic impact of adaptation in a transboundary pollution dynamic game
Bibliographic record
Abstract
This work studies the strategic impact of a region’s investment in adaptation measures on the equilibrium outcomes of a transboundary pollution dynamic game played in finite horizon. We incorporate adaptation as a region-specific capital stock that decreases local damages and study the feedback (subgame perfect) equilibrium of the non-cooperative game between two regions. In order to discern the impact of adaptation, we compare the equilibrium solutions of three scenarios, which differ in the regions’ ability to invest in adaptation measures. The results show that investing in adaptation gives regions an incentive to increase their emissions, which causes an inverse strategic response in the other region. The anticipation of a rise in pollution makes the other region respond by cutting its emissions and investing more in adaptation. The equilibrium trajectories of the stocks of pollution and adaptation capital follow the highest path over time when both regions adapt. When there is an asymmetry between regions in their adaptation capabilities, the region that does not (or cannot) adapt becomes worse off due to lower emissions and higher damages, while the adapting region finishes the game better off than the no-adaptation case.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".